| bibtype |
J -
Journal Article
|
| ARLID |
0570900 |
| utime |
20240402213757.4 |
| mtime |
20230412235959.9 |
| SCOPUS |
85153355712 |
| WOS |
000975099100002 |
| DOI |
10.1109/TSIPN.2023.3264990 |
| title
(primary) (eng) |
Robust Online Modeling of Counts in Agent Networks |
| specification |
| page_count |
12 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0519968 |
| ISSN |
2373-776X |
| title
|
IEEE Transactions on Signal and Information Processing over Networks |
| volume_id |
9 |
| volume |
1 (2023) |
| page_num |
217-228 |
|
| keyword |
Diffusion |
| keyword |
Distributed estimation |
| keyword |
Poisson regression |
| author
(primary) |
| ARLID |
cav_un_auth*0247754 |
| name1 |
Žemlička |
| name2 |
R. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0242543 |
| name1 |
Dedecius |
| name2 |
Kamil |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| abstract
(eng) |
Many real-world processes of interest produce nonnegative integer values standing for counts. For instance, we count packets in computer networks, people in monitored areas, or particles incident on detectors. Often, the ultimate goal is the modeling of these counts. However, standard techniques are computationally demanding and sensitive to the amount of available information. In our quest to solve the objective, we consider two prominent features of the contemporary world: online processing of streaming data, and the rapidly evolving ad-hoc agent networks. We propose a novel algorithm for a collaborative online estimation of the zero-inflated Poisson mixture models in diffusion networks. Its main features are low memory and computational requirements, and the capability of running in inhomogeneous networks. There, the agents possibly observe different processes, and locally decide which of their neighbors provide useful information. Two simulation examples demonstrate that the algorithm attains good stability and estimation performance even under slowly varying parameters. |
| result_subspec |
WOS |
| RIV |
IN |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20205 |
| reportyear |
2024 |
| num_of_auth |
2 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0342468 |
| confidential |
S |
| mrcbC91 |
C |
| mrcbT16-e |
ENGINEERINGELECTRICALELECTRONIC|TELECOMMUNICATIONS |
| mrcbT16-j |
1.29 |
| mrcbT16-D |
Q1 |
| arlyear |
2023 |
| mrcbU14 |
85153355712 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000975099100002 WOS |
| mrcbU63 |
cav_un_epca*0519968 IEEE Transactions on Signal and Information Processing over Networks Roč. 9 č. 1 2023 217 228 2373-776X 2373-776X |
|